Search suggestions of related entities based on co-occurrence and/or fuzzy-score matching

ABSTRACT

A method for generating search suggestions of related entities based on co-occurrence and/or fuzzy score matching is disclosed. The method may be employed in a search system that may include a client/server type architecture. The search system may include a user interface for a search engine in communication with one or more server devices over a network connection. The server device may include an entity extraction module, a fuzzy-score matching module, and an entity co-occurrence knowledge base database. In one embodiment, the search system may process a partial search query from a user and present search suggestions to complete the partial query. In another embodiment, the complete search query may be used as a new search query. The search system may process the new search query, run an entity extraction, find related entities from the entity co-occurrence knowledge base, and present said related entities in a drop down list.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application is a continuation of U.S. patent application Ser. No.14/920,580, entitled “Search Suggestions of Related Entities Based onCo-Occurrence and/or Fuzzy-Score Matching,” filed on Oct. 22, 2015,which is a continuation of U.S. patent application Ser. No. 14/558,159entitled “Search Suggestions of Related Entities Based on Co-Occurrenceand/or Fuzzy-Score Matching,” filed Dec. 2, 2014, which claims a benefitof priority to U.S. Provisional Application 61/910,905, filed Dec. 2,2013, entitled “Search Suggestions of Related Entities Based onCo-Occurrence and/or Fuzzy-Score Matching,” each of which areincorporated herein by reference in their entirety for all purposes.

This application is related to U.S. patent application Ser. No.14/557,794, entitled “Method for Disambiguating Features in UnstructuredText,” filed Dec. 2, 2014; U.S. patent application Ser. No. 14/558,300,entitled “Event Detection Through Text Analysis Using Trained EventTemplate Models,” filed Dec. 2, 2014; U.S. patent application Ser. No.14/558,101, entitled “Non-Exclusionary Search Within In-MemoryDatabases,” filed Dec. 2, 2014; and U.S. patent application Ser. No.14/558,036, entitled “Search Suggestions Fuzzy-Score Matching and EntityCo-Occurrence,” filed Dec. 2, 2014; each of which are incorporatedherein by reference in their entirety.

TECHNICAL FIELD

The present disclosure relates generally to computer query processing,and more specifically to electronic search suggestions of relatedentities based on co-occurrence and/or fuzzy score matching.

BACKGROUND

Users frequently use search engines for locating information of interesteither from the Internet or any database system. Search engines commonlyoperate by receiving a search query from a user and returning searchresults to the user. Search results are usually ordered based on therelevance of each returned search result to the search query. Therefore,the quality of the search query may be significantly important for thequality of search results. However, search queries from users, in mostcases, may be written incomplete or partial (e.g., the search query maynot include enough words to generate a focused set of relevant resultsand instead generates a large number of irrelevant results), andsometimes misspelled (e.g., Bill Smith may be incorrectly spelled as“Bill Smitth”).

One common approach to improve the quality of the search results is toenhance the search query. One way to enhance the search query may be bygenerating possible suggestions based on the user's input. For this,some approaches propose methods for identifying candidate queryrefinements for a given query from past queries submitted by one or moreusers. However, these approaches are based on query logs that sometimesmay lead the user to results that may not be of interest. There areother approaches using different techniques that may not be accurateenough. Thus, there still exists a need for methods that improve orenhance search queries from users to get more accurate results and alsopresent users with useful related entities of interest as they type thesearch query.

SUMMARY

A method for generating search suggestions of related entities based onco-occurrence and/or fuzzy score matching is disclosed. In one aspect ofthe present disclosure, the method may be employed in a computer searchsystem that may include a client/server type architecture. In oneembodiment, the search system may include a user interface to a searchengine in communication with one or more server devices over a networkconnection. The server device may include one or more processorsexecuting instructions for a plurality of special purpose computermodules, including an entity extraction module and a fuzzy-scorematching module, as well as an entity co-occurrence knowledge basedatabase. The knowledge base may be built as an in-memory database andmay also include other components, such as one or more searchcontrollers, multiple search nodes, collections of compressed data, anda disambiguation module. One search controller may be selectivelyassociated with one or more search nodes. Each search node may becapable of independently performing a fuzzy key search through acollection of compressed data and returning a set of scored results toits associated search controller.

In another aspect of the present disclosure, the method may includeperforming partial entity extractions, by an entity extraction module,from provided search queries to identify whether the search query refersto an entity, and if so, to determine the entity type. Furthermore, themethod may include generating algorithms, by a fuzzy-score matchingmodule, corresponding to the type of entity extracted and performing asearch against an entity co-occurrence knowledge base. Additionally, thequery text parts that are not detected as entities are treated asconceptual features, such as topics, facts, and key phrases that can beemployed for searching the entity co-occurrence knowledge base. Theentity co-occurrence knowledge base, which may already have a repositorywhere entities may be indexed as entities to entities, entities totopics, or entities to facts, among others, may return fast and accuratesuggestions to the user to complete the search query.

In a further aspect of the present disclosure, the completed searchquery may be used as a new search query. The search system may processthe new search query, run an entity extraction, find related entitieswith the highest scores from the entity co-occurrence knowledge base,and present said related entities in a drop down list that may be usefulfor the user.

In one embodiment, a method is disclosed. The method comprisesreceiving, by an entity extraction computer, user input of partialsearch query parameters from a user interface, the partial search queryparameters having at least one incomplete search query parameter,extracting, by the entity extraction computer, one or more firstentities from the partial search query parameters by comparing thepartial search query parameters with an entity co-occurrence databasehaving instances of co-occurrence of the one or more first entities inan electronic data corpus and identifying at least one entity typecorresponding to the one or more first entities in the partial searchquery parameters, and selecting, by a fuzzy-score matching computer, afuzzy matching algorithm for searching the entity co-occurrence databaseto identify one or more records associated with the partial search queryparameters, wherein the fuzzy matching algorithm corresponds to the atleast one identified entity type. The method further includes searching,by the fuzzy-score matching computer, the entity co-occurrence databaseusing the selected fuzzy matching algorithm and forming one or morefirst suggested search query parameters from the one or more recordsbased on the search, presenting, by the fuzzy-score matching computer,the one or more first suggested search query parameters via the userinterface, receiving by the entity extraction computer, user selectionof the one or more first suggested search query parameters so as to formcompleted search query parameters, and extracting, by the entityextraction computer, one or more second entities from the completedsearch query parameters. The method further includes searching, by theentity extraction computer, the entity co-occurrence database toidentify one or more entities related to the one or more second entitiesso as to form one or more second suggested search query parameters, andpresenting, by the entity extraction computer, the one or more secondsuggested search query parameters via the user interface.

In another embodiment, a system is disclosed. The system comprises oneor more server computers having one or more processors executingcomputer readable instructions for a plurality of computer modulesincluding an entity extraction module configured to receive user inputof partial search query parameters from a user interface, the partialsearch query parameters having at least one incomplete search queryparameter, the entity extraction module being further configured toextract one or more first entities from the partial search queryparameters by comparing the partial search query parameters with anentity co-occurrence database having instances of co-occurrence of theone or more first entities in an electronic data corpus and identifyingat least one entity type corresponding to the one or more first entitiesin the partial search query parameters. The system further includes afuzzy-score matching module configured to select a fuzzy matchingalgorithm for searching the entity co-occurrence database to identifyone or more records associated with the partial search query parameters,wherein the fuzzy matching algorithm corresponds to the at least oneidentified entity type. The fuzzy-score matching module is furtherconfigured to search the entity co-occurrence database using theselected fuzzy matching algorithm and form one or more first suggestedsearch query parameters from the one or more records based on thesearch, and present the one or more first suggested search queryparameters via the user interface. Additionally, the entity extractionmodule is further configured to receive user selection of the one ormore first suggested search query parameters so as to form completedsearch query parameters, extract one or more second entities from thecompleted search query parameters, search the entity co-occurrencedatabase to identify one or more entities related to the one or moresecond entities so as to form one or more second suggested search queryparameters, and present the one or more second suggested search queryparameters via the user interface.

Definitions

As used here, the following terms may have the following definitions:

“Entity extraction” refers to computer information processing methodsfor extracting information such as names, places, and organizations.

“Corpus” refers to a collection of one or more electronic documents.

“Features” is any information which is at least partially derived from adocument.

“Module” refers to one or more computer hardware and/or softwarecomponents suitable for carrying out at least one or more tasks.

“Fact” refers to objective relationships between features.

“Entity knowledge base” refers to an electronic database containingfeatures/entities.

“Query” refers to an electronic request to retrieve information from oneor more suitable databases.

“Topic” refers to a set of thematic information which is at leastpartially derived from a corpus.

BRIEF DESCRIPTION OF THE DRAWINGS

The present disclosure can be better understood by referring to thefollowing figures. The components in the figures are not necessarily toscale, emphasis instead being placed upon illustrating the principles ofthe disclosure. In the figures, reference numerals designatecorresponding parts throughout the different views.

FIG. 1 is a block diagram illustrating an exemplary system environmentin which one embodiment of the present disclosure may operate.

FIG. 2 is a flowchart illustrating a method for generating searchsuggestions of related entities based on co-occurrence and/or fuzzyscore matching, according to an embodiment.

FIG. 3 is an example embodiment of a user interface associated with themethod described in FIG. 2.

DETAILED DESCRIPTION

The present disclosure is herein described in detail with reference toembodiments illustrated in the drawings, which form a part hereof. Otherembodiments may be used and/or other changes may be made withoutdeparting from the spirit or scope of the present disclosure. Theillustrative embodiments described in the detailed description are notmeant to be limiting of the subject matter presented herein.

Reference will now be made to the exemplary embodiments illustrated inthe drawings, and specific language will be used herein to describe thesame. It will nevertheless be understood that no limitation of the scopeof the invention is thereby intended. Alterations and furthermodifications of the inventive features illustrated herein, andadditional applications of the principles of the inventions asillustrated herein, which would occur to one skilled in the relevant artand having possession of this disclosure, are to be considered withinthe scope of the present disclosure.

Embodiments of the present disclosure introduce a novel electronicsearch suggestion generation mechanism which is different from theexisting mechanisms that are based on mining and ranking the activity ofthe search system's global users' past search queries. The presentedsearch suggestion mechanism, is based on employing an entityco-occurrence knowledge base in its core, along with fuzzy matchingmodules and entity extraction modules. The entity co-occurrenceknowledge base, is an electronic repository where entities may beindexed as entities to entities, entities to topics, or entities tofacts among others, and stored in a way to allow faster and weightedresponses. In brief, the user partial/complete queries are processedon-the-fly to detect entities (entity extraction), misspelled variations(fuzzy matching) of the entities and other conceptual features. Thesefeatures are employed to search (fuzzy score matching) entityco-occurrence knowledge base to suggest search queries and possibleexpansions/suggestions of the accurate entities intended by the user,which will lead to a more accurate search experience. Further, once thesuggested entity is chosen, the proposed system would suggest relatedentities that are present in the entity co-occurrence knowledge base,which will lead to an improved consecutive search experience, asdiscussed in further detail in FIGS. 1-3 below.

FIG. 1 is a block diagram of a search system 100 in accordance with thepresent disclosure. The search system 100 may include one or more userinterfaces 102 to a search engine 104 in communication with a serverdevice 106 over a network 108. In this embodiment, the search system 100may be implemented in a client/server type architecture; however, thesearch system 100 may be implemented using other computer architectures(for example, a stand-alone computer, a mainframe system with terminals,an ASP model, a peer to peer model and the like) and a plurality ofnetworks such as, a local area network, a wide area network, theinternet, a wireless network, a mobile phone network and the like.

A search engine 104 may include, but is not limited to, an interface viaa web-based tool that enables users to locate information on the WorldWide Web. Search engine 104 may also include tools that enable users tolocate information within internal database systems. Server device 106,which may be implemented in a single server device 106 or in adistributed architecture across a plurality of server computers, mayinclude an entity extraction module 110, a fuzzy-score matching module112, and an entity co-occurrence knowledge base database 114.

Entity extraction module 110 may be a hardware and/or software computermodule able to extract and disambiguate on-the-fly independent entitiesfrom a given set of queries such as a query string, partial query,structured data and the like. Example of entities may include people,organizations, geographic locations, dates and/or time. During theextraction, one or more feature recognition and extraction algorithmsmay be employed. Also, a score may be assigned to each extractedfeature, indicating the level of certainty of the feature beingcorrectly extracted with the correct attributes. Taking into account thefeature attributes, the relative weight or relevance of each of thefeatures may be determined. Additionally, the relevance of theassociation between features may be determined using a weighted scoringmodel.

Fuzzy-score matching module 112 may include a plurality of algorithmsthat may be adjusted or selected according to the type of entityextracted from a given search query. The function of the algorithms maybe to determine whether the given search query (input) and suggestedsearched strings are similar to each other, or approximately match agiven pattern string. Fuzzy matching may also be known as fuzzy stringmatching, inexact matching, and approximate matching. Entity extractionmodule 110 and fuzzy-score matching module 112 may work in conjunctionwith Entity co-occurrence knowledge base 114 to generate searchsuggestions for the user.

According to various embodiments, entity co-occurrence knowledge base114 may be built, but is not limited to, as an in-memory database andmay include components such as one or more search controllers, multiplesearch nodes, collections of compressed data, and a disambiguationmodule. One search controller may be selectively associated with one ormore search nodes. Each search node may be capable of independentlyperforming a fuzzy key search through a collection of compressed dataand returning a set of scored results to its associated searchcontroller.

Entity co-occurrence knowledge base 114 may include related entitiesbased on features and ranked by a confidence score. Various methods forlinking the features may be employed, which may essentially use aweighted model for determining which entity types are most important,which have more weight, and, based on confidence scores, determine howconfident the extraction of the correct features has been performed.

FIG. 2 is a flowchart illustrating an embodiment of a method 200 forgenerating search suggestions of related entities based on co-occurrenceand/or fuzzy score matching. Method 200 may be implemented in a searchsystem 100, similar to as described in FIG. 1.

In one embodiment, method 200 may initiate when a user starts typing asearch query, step 202, in the search engine 104, as described above inFIG. 1. As the search query is typed, search system 100 may perform anon-the-fly process. According to various embodiments, search query maybe complete and/or partial, correctly spelled and/or misspelled. Next, apartial entity extraction step 204 of search query may be performed. Thepartial entity extraction step 204 may run a quick search against entityco-occurrence knowledge base 114 to identify whether the search queryincludes an entity and, if so, the entity type. According to variousembodiments, search query entity may refer to a person, an organization,the location of a place, and a date among others. Once the entity is, afuzzy-score matching module 112 may select a corresponding fuzzymatching algorithm, step 206. For example, if search query wasidentified as an entity that is referring to a person, then fuzzy-scorematching module 112 may adjust or select the string matching algorithmfor persons, which can extract different components of the person'sname, including first, middle, last, and title. In another embodiment,if search query was identified as an entity that is referring to anorganization, then fuzzy-score matching module 112 may adjust or selectthe string matching algorithm for organizations, which can includeidentifying terms such as school, university, corp., and inc.Fuzzy-score matching module 112 therefore adjusts or selects the stringmatching algorithm for the type of entity in order to facilitate thesearch. Once the string matching algorithm is adjusted or selected tocorrespond to the type of entity, a fuzzy-score matching may beperformed in step 208.

In fuzzy-score matching step 208, extracted entity or entities, as wellas any non-entities, may be searched and compared against entityco-occurrence knowledge base 114. Extracted entity or entities mayinclude incomplete names of persons, for example first name and thefirst character of the last name, abbreviations of organizations, forexample “UN” that may stand for “United Nations”, short forms, andnicknames among others. Entity co-occurrence knowledge base 114 mayalready have registered a plurality of records indexed in an structureddata, such as entity to entity, entity to topics, and entity to factsindex among others. This may allow fuzzy-score matching in step 208 tohappen expeditiously. Fuzzy-score matching may use, but is not limitedto, a common string metric such as Levenshtein distance, strcmp95, ITFscoring, and the like. Levenshtein distance between two words may referto the minimum number of single-character edits required to change oneword into the other.

Once fuzzy-score matching in step 208 step finishes comparing andsearching the search query against all records in the entityco-occurrence knowledge base 114, the record that dominates the most oris the closest to match the given pattern string of the search queryinput may be selected as first candidate for a search suggestion, step210. Other records with less proximity to match the given pattern stringof the search query input may be placed under the first candidate in adescending order. Search suggestion in step 210 may then be presented tothe user in a drop down list of possible matches that the user mayselect to complete the query.

In another embodiment, after the user selects a match of his/herinterest, search system 100 may take that selection as a new searchquery, step 212. Subsequently, an entity extraction step 214 from saidnew search query may be performed. During the extraction, one or morefeature recognition and extraction algorithms may be employed. Also, ascore may be assigned to each extracted feature, indicating the level ofcertainty of the feature being correctly extracted with the correctattributes. Taking into account the feature attributes, the relativeweight or relevance of each of the features may be determined.Additionally, the relevance of the association between features may bedetermined using a weighted scoring model. Entity extraction module 110may then run a search against entity co-occurrence knowledge base 114 tofind related entities, step 216, based on the co-occurrences with thehighest scores. Finally, a drop down list of search suggestions, in step218, including related entities, may be presented to the user beforeperforming the actual search of the data in the electronic documentcorpus.

FIG. 3 is an example embodiment of a user interface 300 associated withthe method 200 for generating search suggestions of related entitiesbased on co-occurrence and/or fuzzy score matching. In this example, auser through a search engine interface 302, similar to that described byFIG. 1, inputs a partial query 304 in a search box 306. By a way ofillustration and not by way of limitation, partial query 304 may be anincomplete name of a person such as “Michael J”, as shown in FIG. 3. Itmay be considered a partial query 304 because the user may not have yetselected search button 308, or otherwise submitted the partial query 304to search system 100 to perform an actual search and obtain results.

Following the method 200, as the user types “Michael J”, the entityextraction module 110 performs a quick search on-the-fly of the firstword (Michael) against entity co-occurrence knowledge base 114 toidentify the type of entity, in this example, the entity may refer tothe name of a person. Subsequently, fuzzy-score matching module 112 mayselect a string match algorithm tailored for names of persons. Name ofpersons may be found in databases written in different forms such asusing only initials (short forms), or first name and first character ofthe last name, or first name, initial of the middle name and last name,or any combination thereof. Fuzzy-score matching module 112 may use acommon string metric such as Levenshtein distance to determine andassign a score to the entity, topic, or fact within entity co-occurrenceknowledge base 114 that may match the entity “Michael”. In this example,Michael matches with a great amount of records having that name.However, as the user types the following character “J”, fuzzy-scorematching module 112 may perform another comparison based on Levenshteindistance against all co-occurrences with Michael with the entityco-occurrence knowledge base 114. Entity co-occurrence knowledge base114 may then select all possible matches with the highest scores for“Michael J”. For example, fuzzy-score matching module 112 may returnsearch suggestions 310 to complete “Michael J” such as “MichaelJackson”, “Michael Jordan”, “Michael J. Fox”, or even “Michael Dell” insome cases to the user. The user may then be able to either select fromthe drop down list one of the persons suggested, or ignore thesuggestion and keep typing. Expanding on the aforementioned example, aquery like “Michael the basketball player”, would lead to the suggestionof “Michael Jordan”, based on the results returned by searching Entityco-occurrence knowledge base for “Michael” in person entity namevariations and “the basketball player” in the co-occurrence featureslike key phrases, facts, topics, and the like. Another example can be“Alexander the actor”, would lead to the suggestion of “AlexanderPolinsky”. As those skilled in the art will realize, the existing searchplatforms cannot provide suggestions generated in the aforementionedmanner.

In this embodiment, the user may select “Michael Jordan” from the dropdown list to complete the partial query 304, as indicated in FIG. 3.Said selection may then be processed as a new search query 312 by searchsystem 100. Subsequently, an entity extraction from said new searchquery 312 may be performed. During the extraction, one or more featurerecognition and extraction algorithms may be employed. Also, a score maybe assigned to each extracted feature, indicating the level of certaintyof the feature being correctly extracted with the correct attributes.Taking into account the feature attributes, the relative weight orrelevance of each of the features may be determined. Additionally, therelevance of the association between features may be determined using aweighted scoring model. Entity extraction module 110 may then run asearch for “Michael Jordan” against entity co-occurrence knowledge base114 to find related entities based on the co-occurrences with thehighest scores. Finally, a drop down list of search suggestions 314,including related entities, may be presented to the user beforeperforming the actual search by clicking on the search button 308. Theforegoing system and method described in FIGS. 1-3 may be fast andconvenient for the user since the user may find useful relationships.

While various aspects and embodiments have been disclosed, other aspectsand embodiments are contemplated. The various aspects and embodimentsdisclosed are for purposes of illustration and are not intended to belimiting, with the true scope and spirit being indicated by thefollowing claims.

The foregoing method descriptions and the process flow diagrams areprovided merely as illustrative examples and are not intended to requireor imply that the steps of the various embodiments must be performed inthe order presented. As will be appreciated by one of skill in the artthe steps in the foregoing embodiments may be performed in any order.Words such as “then,” “next,” etc. are not intended to limit the orderof the steps; these words are simply used to guide the reader throughthe description of the methods. Although process flow diagrams maydescribe the operations as a sequential process, many of the operationscan be performed in parallel or concurrently. In addition, the order ofthe operations may be re-arranged. A process may correspond to a method,a function, a procedure, a subroutine, a subprogram, etc. When a processcorresponds to a function, its termination may correspond to a return ofthe function to the calling function or the main function.

The various illustrative logical blocks, modules, circuits, andalgorithm steps described in connection with the embodiments disclosedhere may be implemented as electronic hardware, computer software, orcombinations of both. To clearly illustrate this interchangeability ofhardware and software, various illustrative components, blocks, modules,circuits, and steps have been described above generally in terms oftheir functionality. Whether such functionality is implemented ashardware or software depends upon the particular application and designconstraints imposed on the overall system. Skilled artisans mayimplement the described functionality in varying ways for eachparticular application, but such implementation decisions should not beinterpreted as causing a departure from the scope of the presentinvention.

Embodiments implemented in computer software may be implemented insoftware, firmware, middleware, microcode, hardware descriptionlanguages, or any combination thereof. A code segment ormachine-executable instructions may represent a procedure, a function, asubprogram, a program, a routine, a subroutine, a module, a softwarepackage, a class, or any combination of instructions, data structures,or program statements. A code segment may be coupled to another codesegment or a hardware circuit by passing and/or receiving information,data, arguments, parameters, or memory contents. Information, arguments,parameters, data, etc. may be passed, forwarded, or transmitted via anysuitable means including memory sharing, message passing, token passing,network transmission, etc.

The actual software code or specialized control hardware used toimplement these systems and methods is not limiting of the invention.Thus, the operation and behavior of the systems and methods weredescribed without reference to the specific software code beingunderstood that software and control hardware can be designed toimplement the systems and methods based on the description here.

When implemented in software, the functions may be stored as one or moreinstructions or code on a non-transitory computer-readable orprocessor-readable storage medium. The steps of a method or algorithmdisclosed here may be embodied in a processor-executable software modulewhich may reside on a computer-readable or processor-readable storagemedium. A non-transitory computer-readable or processor-readable mediaincludes both computer storage media and tangible storage media thatfacilitate transfer of a computer program from one place to another. Anon-transitory processor-readable storage media may be any availablemedia that may be accessed by a computer. By way of example, and notlimitation, such non-transitory processor-readable media may compriseRAM, ROM, EEPROM, CD-ROM or other optical disk storage, magnetic diskstorage or other magnetic storage devices, or any other tangible storagemedium that may be used to store desired program code in the form ofinstructions or data structures and that may be accessed by a computeror processor. Disk and disc, as used here, include compact disc (CD),laser disc, optical disc, digital versatile disc (DVD), floppy disk, andBlu-ray disc where disks usually reproduce data magnetically, whilediscs reproduce data optically with lasers. Combinations of the aboveshould also be included within the scope of computer-readable media.Additionally, the operations of a method or algorithm may reside as oneor any combination or set of codes and/or instructions on anon-transitory processor-readable medium and/or computer-readablemedium, which may be incorporated into a computer program product.

The preceding description of the disclosed embodiments is provided toenable any person skilled in the art to make or use the presentinvention. Various modifications to these embodiments will be readilyapparent to those skilled in the art, and the generic principles definedhere may be applied to other embodiments without departing from thespirit or scope of the invention. Thus, the present invention is notintended to be limited to the embodiments shown here but is to beaccorded the widest scope consistent with the following claims and theprinciples and novel features disclosed here.

What is claimed is:
 1. A method comprising: identifying, by a server, afirst entity in an incomplete search query parameter based on querying alocal in-memory database for an entity type corresponding to the firstentity; searching, by the server, the local in-memory database via afuzzy matching process corresponding to the entity type such that arecord is located, wherein the record is associated with the incompletesearch query parameter based on a confidence score and a ranking basedon the confidence score; sending, by the server, a first suggestedsearch query parameter to the client as formed based on the record;forming, by the server, a completed search query parameter based on aselection from the client, wherein the selection selects the firstsuggested search query parameter; extracting, by the server, a secondentity from the completed search query parameter; identifying, by theserver, a third entity in the local in-memory database, wherein thethird entity is related to the second entity; and sending, by theserver, a second suggested search query parameter to the client, whereinthe second suggested search query parameter is based on the thirdentity.
 2. The method of claim 1, further comprising: extracting, by theserver, a feature from the local in-memory database; and assigning, bythe server, a score to the feature, wherein the score indicates a levelof certainty of the feature being extracted with a correct attribute. 3.The method of claim 1, wherein the searching is before a search query isfinalized, wherein the search query includes the incomplete search queryparameter.
 4. The method of claim 1, wherein the record comprises aconceptual feature.
 5. The method of claim 1, wherein the firstsuggested search query parameter comprises a plurality of firstsuggested search query parameters, wherein the method furthercomprising: sorting, by the server, the first suggested search queryparameters in a descending order based on a proximity of a match to theincomplete search query parameter.
 6. The method of claim 4, wherein thesending of the first suggested search query parameter to the client issuch that the first suggested search query parameter is presented on theclient in a drop down list.
 7. The method of claim 1, wherein the localin-memory is indexed.
 8. The method of claim 1, wherein the localin-memory includes an entity-to-entity index.
 9. The method of claim 1,wherein the local in-memory includes an entity-to-topic index.
 10. Themethod of claim 1, wherein the local in-memory includes anentity-to-facts index.
 11. A system comprising: a server configured to:identify a first entity in an incomplete search query parameter based onquerying a local in-memory database for an entity type corresponding tothe first entity; search the local in-memory database via a fuzzymatching process corresponding to the entity type such that a record islocated, wherein the record is associated with the incomplete searchquery parameter based on a confidence score and a ranking based on theconfidence score; send a first suggested search query parameter to theclient as formed based on the record; form a completed search queryparameter based on a selection from the client, wherein the selectionselects the first suggested search query parameter; extract a secondentity from the completed search query parameter; identify a thirdentity in the local in-memory database, wherein the third entity isrelated to the second entity; and send a second suggested search queryparameter to the client, wherein the second suggested search queryparameter is based on the third entity.
 12. The system of claim 11,wherein the server is configured to: extract a feature from the localin-memory database; assign a score to the feature, wherein the scoreindicates a level of certainty of the feature being extracted with acorrect attribute.
 13. The system of claim 11, wherein the server isconfigured to perform the search before a search query is finalized,wherein the search query includes the incomplete search query parameter.14. The system of claim 11, wherein the record comprises a conceptualfeature.
 15. The system of claim 11, wherein the first suggested searchquery parameter comprises a plurality of first suggested search queryparameters, wherein the server is configured to: sort the firstsuggested search query parameters in a descending order based on aproximity of a match to the incomplete search query parameter.
 16. Thesystem of claim 14, wherein the server is configured to send the firstsuggested search query parameter to the client such that the firstsuggested search query parameter is presented on the client in a dropdown list.
 17. The system of claim 11, wherein the local in-memorydatabase is indexed.
 18. The system of claim 11, wherein the localin-memory database includes an entity-to-entity index.
 19. The system ofclaim 11, wherein the local in-memory database includes anentity-to-topic index.
 20. The system of claim 11, wherein the localin-memory database includes an entity-to-facts index.